Vehicle resiliency, driving feedback and risk assessment using machine learning-based vehicle wear scoring

ABSTRACT

A machine learning model is manufactured by a process including retrieving training data, minimizing a loss function, wherein the training data may include labeled or unlabeled data, the machine learning model generating a prediction. A machine learning training/operation server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to retrieve training data, input a training input, analyze the training input to generate a prediction, generate a loss score, and store the trained machine learning model. A method for training a machine learning model includes receiving training data, inputting a training input, analyzing the training input, generating a loss score, and storing the trained machine learning model.

FIELD OF THE DISCLOSURE

The present disclosure is generally directed to modeling aspects ofvehicle operation, and more particularly, for measuring and predictingvehicle resiliency, providing driving feedback, and performing riskprofiling using machine learning-based techniques.

BACKGROUND

The operation of vehicles increasingly generates telematics data.Telematics data include data that represents various aspects of vehicleoperation, including the state of vehicle systems (e.g., a brakingsystem, a suspension system, a coolant system, etc.), in addition tovehicle positional and physical information (e.g., vehicle location,course, speed, etc.).

It has recently become possible to derive high quality telematics datavia mobile computing devices. However, conventional techniques do notinclude using telematics data obtained from mobile computing devices fordetermining vehicle resiliency, for engaging users via driving feedbackor for determining risk. Telematics data on its own, withoutinterpretation, is voluminous and resists interpretation by humans.

BRIEF SUMMARY

In one aspect, a machine learning model is stored on a non-transitorycomputer readable medium, wherein the machine learning model ismanufactured by a process comprising retrieving a training data set andtraining the machine learning model, until a loss score meets apredetermined criteria. The training may include inputting a traininginput and corresponding label, analyzing the training input using themachine learning model to generate a prediction, and generating the lossscore by comparing the prediction to the label using a loss function.The non-transitory computer readable medium may be further manufacturedby storing the trained machine learning model on the non-transitorycomputer readable medium.

In another aspect, a machine learning training and operation serverincludes one or more processors and a memory storing instructions. Whenexecuted by the one or more processors, the instructions may cause theserver to retrieve a training data set; input a training input andcorresponding label, analyze the training input to generate aprediction, and generate a loss score by comparing the prediction to thelabel using a loss function; and store, when the loss score meets apredetermined criteria, the trained machine learning model.

In yet another aspect, a computer-implemented method for training amachine learning model includes receiving, via one or more processors, atraining data set, inputting to the machine learning model a traininginput and corresponding label, analyzing the training input using themachine learning model to generate a prediction, and generating a lossscore by comparing the prediction to the label using a loss function.The method may include storing, when the loss score meets apredetermined criteria, the trained machine learning model.

Depending upon the embodiment, one or more benefits may be achieved.These benefits and various additional objects, features and advantagesof the present disclosure can be fully appreciated with reference to thedetailed description and accompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each figuredepicts one embodiment of a particular aspect of the disclosed systemand methods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals.

FIG. 1 depicts an exemplary computing environment in which techniquesfor computing a vehicle wear score using machine learning, and for usingthe vehicle wear score to model vehicle warranty and vehiclereinsurance, are depicted,

FIG. 2 depicts an exemplary artificial neural network, according to oneembodiment.

FIG. 3 depicts an exemplary neuron of the artificial neural network ofFIG. 2 , according to one embodiment and scenario.

FIG. 4 depicts an exemplary computer-implemented method, according toone embodiment and scenario.

The figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the present disclosure.

DETAILED DESCRIPTION

The embodiments described herein relate to, inter alia, methods andsystems for measuring and predicting vehicle resiliency, providingdriving feedback, and performing risk profiling using one or moremachine learning (ML) models. In some embodiments, vehicle telematicsdata may be generated by a stationary telematics system within avehicle, and/or via a mobile computing device. The present techniquesinclude measuring and predicting vehicle resiliency by training an MLmodel to rank vehicles according to the vehicles' respectivesusceptibility to wear and tear.

The present techniques include simulating wear and tear according tovehicle operator behavior and providing feedback to the vehicleoperator. The present techniques include providing risk assessment, byshowing how driving behavior is correlated to risk over time.

Inputs to the one or more ML models include vehicle type, vehicleoperation behavior, and vehicle age (as expressed as length of ownershipand/or odometer). In an embodiment, vehicle type, vehicle operationbehavior, and vehicle age are manually adjusted by an operator tosimulate partial effects.

In some embodiments, the ML models may analyze historical driving datato simulate how vehicle operation behavior would affect wear and tear ofa vehicle the driver has not yet driven, or how present driving behaviorwould have affected a vehicle driven in the past for which no telematicsdata are available.

In yet further embodiments, an autoencoder may be used to train a deeplearning model that is able to analyze all vehicles in existence.

Exemplary Computing Environment

FIG. 1 depicts an example environment 100 for implementing methodsand/or systems for measuring and predicting vehicle resiliency,providing driving feedback, and performing risk profiling using one ormore machine learning (ML) models. The environment 100 may include avehicle 102 associated with a telematics system 104, a network 106, anda server 108.

The vehicle 102 and the telematics system 104 are remote from the server108 and are coupled to the server 108 via the network 106. The network106 may include any suitable combination of wired and/or wirelesscommunication networks, such as one or more local area networks (LANs),metropolitan area networks (MANs), and/or wide area network (WANs). Asjust one specific example, the network 106 may include a cellularnetwork, the Internet, and a server-side LAN. As another example, thenetwork 106 may support a cellular (e.g., 4G) connection to a mobilecomputing device of a user and an IEEE 802.11 connection to the mobilecomputing device. While referred to herein as a “server,” the server 108may, in some implementations, include multiple servers and/or othercomputing devices. Moreover, the server 108 may include multiple serversand/or other computing devices distributed over a large geographic area(e.g., including devices at one or more data centers), and any of theoperations, computations, etc., described below may be performed in byremote computing devices in a distributed manner.

The telematics system 104 may include hardware and software componentsimplemented in one or more devices permanently and/or temporarilyaffixed to, or otherwise carried on or within, the vehicle 102. Forexample, some or all of the components of the telematics system 104 maybe built into the dash of the vehicle 102 or affixed elsewhere withinthe vehicle 102 (e.g., via an onboard diagnostics port of the vehicle102). In an embodiment, the telematics system 104 may be implementedusing a mobile computing device (e.g., a smart phone of the user). Thetelematics system 104 may include specialized hardware (e.g., one ormore sensors) and computer-executable instructions forretrieving/receiving vehicle telematics data from the vehicle 102. Insome cases, the telematics system 104 may be implemented usingcomponents of the vehicle 102 and a mobile computing device. Forexample, a vehicle telematics generation module may be included in thevehicle 102 and a vehicle telematics collection module may be includedin a mobile computing device of the user, wherein the vehicle telematicscollection module receives and/or retrieves a telematics data set fromthe vehicle telematics generation module. In some embodiments, some orall of the telematics system 104 may be provided by a vehicle rentalcompany, or another operator of a fleet of vehicles (e.g., aride-sharing company).

In an embodiment, a telematics system in the vehicle 102 may collect afirst data set and transmit the first data set to the server 108, whilea second telematics system in the mobile computing device of the usermay collect and transmit a second data set to the server 108. While FIG.1 depicts only a single vehicle 102, the vehicle 102 may be incommunication with numerous other vehicles 102 similar to the vehicle102 via the network 106 and/or other networks. The telematics system 104may include a processor 120, a memory 122, a display 124, a networkinterface 126, and a global positioning system (GPS) unit 128. Theprocessor 120 may be a single processor (e.g.; a central processing unit(CPU)), or may include a set of processors (e.g., a CPU and a graphicsprocessing unit (GPU)). The telematics system 104 may further include asensor 140 and a database 150.

It will be appreciated that the present techniques advantageouslyprovide for the collection, processing and analysis of telematics datacollected from the mobile device of the vehicle operator, Specifically,in some embodiments, the telematics system 104 is implemented using amobile device of the user that is carried into the vehicle by thevehicle operator or a passenger. Therefore, the present techniquesdescribe a system architecture that does not need the telematics system104 to be physically coupled to the vehicle 102. This mobilityrepresents an improvement over conventional computing systems at leastbecause the server 108 is able to receive/retrieve data from thetelematics system 104 even when the vehicle is not in an operationalstate, and the instructions in the memory 122 of the telematics system104 may be upgraded and/or modified independent of the vehicle itself.

The memory 122 may be a computer-readable, non-transitory storage unitor device, or collection of units/devices, that includes persistent(e.g., hard disk) and/or non-persistent memory components. The memory122 may store instructions that are executable on the processor 120 toperform various operations, including the instructions of varioussoftware applications and data generated and/or used by suchapplications. In the example implementation of FIG. 1 , the memory 122stores at least a telematics data collection module 130 and a telematicsdata processing module 132.

Generally, the collection module 130 is executed by the processor 120 tofacilitate collection of telematics data from the vehicle 102 and theprocessing module 132 is executed by the processor 120 to facilitate thebidirectional transmission of telematics data between the telematicssystem 104 and the server 108 via the network 106 (e.g., sendingtelematics data collected from the vehicle 102 to the server 108,receiving requests and responses relating to telematics data from theserver 108, etc.). The processing module 132 may encode and/or otherwisemanipulate (e.g., compress, normalize, filter, etc.) the telematicsdata. In some embodiments, the collection module 130 may includeinstructions for converting sensor data to telematics data, or forjoining sensor data with telematics data. For example, the collectionmodule 130 may merge data retrieved from a sensor of the vehicle 102remote from the telematics system 104 with a sensor located locally withrespect to the telematics system 104.

The display 124 includes hardware, firmware and/or software configuredto enable a user to interact with (i.e., both provide inputs to andperceive outputs of) the telematics system 104. For example, the display124 may include a touchscreen with both display and manual inputcapabilities, Alternatively, or in addition, the display 124 may includea keyboard for accepting user inputs, and/or a microphone (withassociated processing components) that provides voice control/inputcapabilities to the user. In some embodiments, the telematics system 104may include multiple different implementations of the display 124 (e.g.,a first display 124 associated with the vehicle 102 and a second display124 associated with a mobile computing device of the user).

The network interface 126 includes hardware, firmware and/or softwareconfigured to enable the telematics system 104 to wirelessly exchangeelectronic data with the server 108 via the network 106, For example,network interface 126 may include a cellular communication transceiver,a WiFi transceiver, and/or transceivers for one or more other wirelesscommunication technologies (e.g., 4G).

The GPS unit 128 includes hardware, firmware and/or software configuredto enable the telematics system 104 to self-locate using GPS technology(alone, or in combination with the services of server 108 and/or anotherserver not shown in FIG. 1 ). Alternatively, or in addition, thetelematics system 104 may include a unit configured to self-locate, orconfigured to cooperate with a remote server or other device(s) toself-locate, using other, non-GPS technologies (e.g., IP-basedgeolocation).

In some embodiments, the collection module 130 (or other software storedin the memory 122) provides functionality for collecting telematics datafrom the vehicle 102. If the telematics system 104 is a unit integratedin the vehicle (e.g., a head unit providing vehicle dashboard integratednavigation technology), for example, the telematics system 104 mayinclude a hardwired connection (e.g., via a Controller Area Network(CAN) bus) to one or more other units of the vehicle 102. As anotherexample, if the telematics system 104 is a smartphone (or smart watch,etc.), the telematics system 104 may couple to one or more units of thevehicle via a wired connection (e.g., an onboard diagnostics connection)or a wireless connection (e.g., Bluetooth, WiFi, etc.). The processingmodule 132 provides functionality for processing telematics data fromthe vehicle 102. The processing module 132 may retrieve/receive datafrom the collection module 132 and/or the database 150. The collectionmodule 130 may collect data from the sensor 140 and may store collecteddata in the database 150.

The sensor 140 may include one or more sensors associated with thevehicle 102 (e.g., a speedometer sensor, a tire pressure sensor, a brakepad thickness sensor, a suspension ride height sensor, etc.) and/or amobile device of the user (e.g., an accelerometer in a smartphone), Thesensor may provide data (e.g., sensor readings) to one or moreapplications (e.g., the collection module 130). Many types of sensorsmay be used, in some embodiments, such as cameras, video cameras, and/ormicrophones. In an embodiment, the sensor 140 includes the sensors foundin a smart phone (e.g., an accelerometer, a gyroscope, a magnetometer,and/or location services data). In some embodiments the sensor 140 maytransmit sensor data to one or more mobile computing devices.

The database 150 may be any suitable database (e.g., a structured querylanguage (SQL) database, a flat file database, a key/value data store,etc.). The database 150 may include a plurality of database tables forstoring data according to data storage schema. The database 150 mayinclude relational linkages between tables (e.g., one or more foreignkeys, primary keys, etc.), and may allow complex data types such as timeseries data to be stored and queried. The database 150 may include oneor more indices.

The server 108 includes a network interface controller (MC) 160, amemory 162, a ML training module 164, a ML operation module 166, aprofile module 168, a feedback module 170 an input device 180, an outputdevice 182 and a database 190. The MC 160 includes hardware, firmwareand/or software configured to enable the server 108 to exchangeelectronic data (e.g., telematics data) with the telematics system 104via network 106. For example, NIC 160 may include a wired or wirelessrouter, switch, model, etc. The network architecture of the environment100 is simplified for explanatory purposes. However, in someembodiments, the network architecture of environment 100 may includeother components and/or configurations.

The memory 162 is a computer-readable, non-transitory storage unit ordevice, or collection of such units/devices, that may include persistent(e.g., hard disk) and/or non-persistent memory components. The memory162 may store data generated and/or used by the ML training module 164and/or the ML operation module 166, for example.

The ML training module 164 is generally configured to train one or moremachine learning models. The memory 162 may include a module fortransmitting ML outputs of the ML training module 162 to the telematicssystem 104 for display. In some embodiments, the ML model may execute inthe telematics system 104. The ML training module 164 may provide one ormore inputs as training parameters to the ML models.

The ML training module 164 may initialize, train, and/or store any typeof machine learning model, including supervised models and/orunsupervised models. For example, the ML training module 164 may trainany suitable type of artificial neural network, such as a convolutionalneural network, recurrent neural network, generative adversarial networkor feed-forward neural network, for example. The neural network mayinclude a number (e.g., hundreds or thousands) of nodes (i.e., neurons)arranged in multiple layers, with each node processing one or moreinputs (e.g., from the preceding layer, if any) to generate a decision,prediction, or other output. In some embodiments, the machine learningmodel may be tree-based.

The ML training module 164 may retrieve historical data, such as claimsdata of an insurer. The claims data may represent electronic insuranceclaims filed by insurance policyholders, and may include informationrelating to insured assets, such as vehicle types, makes, models, etc.The historical claims data may include data related to outcomes (e.g., acollision, property damage, an injury, etc.), The ML training module 164may use the historical data to train the one or more models.

The ML operation module 166 may be configured for loading, initializing,executing and receiving output from the one or more ML models trained bythe ML training module 164, or by other training modules/programs. TheML operation module 166 may be located in the telematics system 104, insome embodiments. Locating the operation module 166 in the telematicssystem 104 advantageously allows the environment 100 to offload machinelearning model operation and data processing to edge/consumer devices,and allows inputs (e.g., operational inputs) and outputs (e.g.,operational outputs) of the ML model operated by the ML operation module166 to be processed and displayed to the user with decreased latency anddecreased use of server-side resources. Locating the operation module166 in the telematics system further advantageously allows use of thetrained ML model when the telematics system 104 is offline (i.e., whenthe telematics system 104 cannot communicate with the network 106).

The vehicle operator profile module 168 may be configured to generatevehicle operator profiles. Specifically, the vehicle operator profilemay associate an operator with one or more vehicles and telematics datasets. For example, the vehicle operator profile module 168 may maintainan association between a particular vehicle operator (e.g., John), oneor more vehicles (e.g., a 2018 Ford Explorer) and telematics data for atime period (e.g., 2019). The vehicle operator profile module 168 maystore the associated data in a database such as the database 190, Inthis way, another module (e.g., the ML training module 164) may querythe database 190 to retrieve a specific slice of telematics datacorresponding to a particular vehicle operator's operation of aparticular vehicle.

The feedback module 170 may include instructions for generating one ormore notifications for display in a mobile computing device of thevehicle operator, such as the display 124 of FIG. 1 . The feedbackmodule 170 may include additional instructions for transmitting thenotification and for receiving inputs from the vehicle operator (e.g.,an acknowledgement message, an input parameter, etc.). The feedbackmodule 170 may process the inputs from the vehicle operator.

The input device 180 and the output device 182 include hardware,firmware and/or software configured to respectively enable the user toprovide inputs to and perceive outputs of the telematics system 104. Inan embodiment, the input device 180 and output device 182 may becombined and implemented as a touchscreen with both display and manualinput capabilities, Alternatively, or in addition, the input device 180may include a keyboard for accepting user inputs, and/or a microphone(with associated processing components) that provides voicecontrol/input capabilities to the user. The output device 182 mayinclude one or more display screens. In some embodiments, the server 108may include multiple different implementations of the input device 180and the output device 182.

The database 190 may be any suitable database (e.g., a structured querylanguage (SQL) database, a flat file database, a key/value data store,etc), The database 190 may include a plurality of database tables forstoring data according to data storage schema. The database 190 mayinclude relational linkages between tables (e.g., one or more foreignkeys, primary keys, etc.), and may allow complex data types such as timeseries data to be stored and queried. The database 190 may include oneor more indices. The database 190 may store profile information,training data, trained ML models/weights.

Exemplary Machine Learning Model Training

Training of various ML models will now be discussed with respect to theenvironment 100 of FIG. 1 . The ML training module 164 may includecomputer-executable instructions for training one or more ML model usingtelematics data. In general, the ML module 164 may train one or more MLmodels by establishing a network architecture, or topology, and addinglayers that may be associated with one or more activation functions(e.g., a rectified linear unit, softmax, etc.), loss functions and/oroptimization functions. One or more types of artificial neural networksmay be employed, including without limitation, recurrent neuralnetworks, convolutional neural networks, and/or deep learning neuralnetworks. Data sets used to train the artificial neural network(s) maybe divided into training, validation, and testing subsets, and thesesubsets may be encoded in an N-dimensional tensor, array, matrix, orother suitable data structures. In supervised learning, training may beperformed by iteratively training the network using labeled trainingsamples. The labels may represent desired (e.g., labeled) outputs forinputs that are similar to the labeled training samples. In this way,the network is able to learn to make predictions or identify features ofde novo inputs that were not used for training.

Training of the artificial neural network may produce byproduct weights,or parameters which may be initialized to random values, and/or manuallyadjusted. The weights may be modified as the network is iterativelytrained, by using one of several gradient descent algorithms, to reduceloss and to cause the values output by the network to converge toexpected, or “learned”, values. In an embodiment, a regression neuralnetwork may be selected which lacks an activation function, whereininput data may be normalized by mean centering, to determine loss andquantify the accuracy of outputs. Such normalization may use a meansquared error loss function and mean absolute error. The artificialneural network model may be validated and cross-validated using standardtechniques such as hold-out, K-fold, etc. In some embodiments, multipleartificial neural networks may be separately trained and operated,and/or separately trained and operated in conjunction. In anotherembodiment, for example, a Bayesian model may be used to train the MLmodel.

FIG. 2 depicts an example ANN 200. The ANN 200 may be initialized (i.e.,created) and trained by the machine learning training module 164 of FIG.1 , in some embodiments. The ANN 200 may execute in the memory of acomputing device (e.g., the server 108) and may analyze one or more dataset. The data sets may be labeled or unlabeled. For example, a data setmay correspond to labeled telematics data or unlabeled telematics data.The ANN 200 may be operated by the ML operation module 164 of FIG. 1 ,for example.

The ANN 200 includes an input layer 204, one or more hidden layers 206,and an output layer 208. Each of the layers in the example neuralnetwork 200 may include an arbitrary number of neurons. For example, theinput layer 204 is depicted as comprising three neurons, however, anynumber of neurons may be included in the input layer 204. Each of theone or more hidden layers 206 may respectively include any number ofneurons. The respective neurons comprising the hidden layers 206 may beconfigured in different ways. For example, the neurons may be chainedtogether linearly and pass output from one to the next, or may benetworked together such that the neurons communicate input and output ina non-linear way. In general, it should be understood that manyconfigurations and/or connections different from those shown in FIG. 2are possible.

The ANN 200, or another model, may be trained to compute variousinformation. For example, the ML training module 164 may train a firstML model to calculate a vehicle wear score, a second ML model tosimulate wear and tear according to vehicle operator behavior, a thirdML model to generate feedback for the vehicle operator, and a fourth MLmodel to generate a risk assessment demonstrating showing how drivingbehavior is correlated to risk over time.

The input layer 204 may correspond to a large number of input parameters(e.g., one million inputs), in some embodiments, and may be analyzedserially or in parallel. Further, various neurons and/or neuronconnections within the neural network 200 may be initialized with anynumber of weights and/or other training parameters, e.g., as depicted inFIG. 3 (discussed further below). Each of the neurons in the hiddenlayers 206 may analyze one or more of the input parameters from theinput layer 204, and/or one or more outputs from a previous one or moreof the hidden layers, to generate a decision or other output. In someembodiments, multiple ANNs 200 may be connected together to form anensemble ANN. In yet further embodiments, an ANN of a different type(e.g., a convolutional neural network, or CNN) may be coupled to the ANN200, for example, to analyze image-based input.

The output layer 208 may include one or more outputs 210, eachindicating a prediction or result. In some embodiments and/or scenarios,the output layer 208 includes only a single output (e.g., a numberpredicted to be the vehicle wear score). In some embodiments, feedbackfrom a subsequent or previous neuron may be used to identify neuronsthat are of lesser relevance to the determination of the trained outputsof the neural network 200. Further, once the neural network 200 istrained, some useless (or less useful) neurons may be bypassed entirelyto optimize the resource consumption of the neural network 200 and/or toimprove the predictive capabilities of the neural network 200.

FIG. 3 depicts an example neuron 220 that may correspond to one of theneurons in the hidden layers 206 of FIG. 2 , in an embodiment. Forexample, the neuron 220 may correspond to the first neuron of the inputlayer 204 of FIG. 2 . Each of the inputs to the neuron 220 may beweighted according to a set of weights W1 through Wi, determined duringthe training process (for example, if the neural network 200 is arecurrent neural network) and then applied to a node 222 that performsan operation α. The operation a may include computing a sum, adifference, a multiple, or a different operation. In some cases, theinitial weights may be manually adjusted.

In some embodiments, weights are not determined for some inputs,notwithstanding the fact that FIG. 3 depicts all inputs X1 through Xn asbeing associated with a weight. Further, the neuron 220 may not considersome inputs as relevant to the determination of outputs, and may thusignore them (e.g., by setting the respective weight to zero). The sum ofthe weighted inputs, r1, may be input to a function 224, labeled in FIG.3B as f1, 1(r1) which may represent any suitable functional operation onr1. As depicted in FIG. 3 , the output of the function 224 may beprovided to a number of neurons of a subsequent layer or as the output210 of the neural network 200.

It should be appreciated that in other embodiments or configurations,the neuron 220 may be arranged differently than the depiction in FIG. 3. For example, the node 222 may be omitted and/or the function 224 maywork directly with the inputs X1 through Xn. There may be a lack of anyweighting, and the operation a may comprise a transforming function,such as taking an absolute value or conversion to a natural number, forexample. The exact manner in which the neural network 200 constitutesand uses layers, and neurons within the layers, may vary depending onthe nature of the input data and/or the desired output (e.g., groundtruth), The structure of the individual layers and/or neurons, includingwithout limitation the type, number, weightings, and so on, may affectthe manner in which the overall neural network 200 functions and thepurpose for the network.

For example, in some embodiments, vehicles may be grouped by vehicleidentifiers (e.g., a key comprising the vehicle year, the vehicle make,and the vehicle model). In other embodiments, vehicles may be groupedaccording to other characteristics. For example, in an embodiment, theML training module 164 may train an unsupervised learning model toperform cluster analysis, wherein the cluster analysis includes groupingvehicles according to vehicle features/attributes (e.g., engine type,square footage, vehicle tonnage, stopping speed, etc.). Such groupingsmay be further analyzed.

It will be appreciated by those of skill in the art that the presenttechniques may include the application of machine learning techniquesother than artificial neural networks. For example, in one embodiment, atree-based machine learning model is used that does not include anyweights. Such a machine learning model may be a decision tree (e.g., aclassification tree, a regression tree, a boosted tree, a random forestclassifier, etc.). In still further embodiments, other techniques may beused, such as support vector machines, regression, Bayesian modeling,and/or genetic algorithms. As noted above, the machine learning modelingmay be supervised or unsupervised, and other types of learning may beimplemented, such as reinforcement learning, self-learning, etc.Specifically, the ML training module 164 may train one or more machinelearning models in addition to, or instead of, the ANN 200.

Exemplary Vehicle Wear Score Modeling Embodiment

In operation, the present techniques may train and operate one or moresupervised machine learning models to accept a target (e.g., lossesattributable to wear and tear) and map features (e.g., specialinteraction between type of vehicle, driving behavior, length ofownership, etc.) to that target. Specifically, the one or more neuronsof the input layer 204 may correspond respectively to input parameterssuch as values measured from one or more sensors of a vehicle. Forexample, the collection module 130 may obtain telematics data and/orsensor data, and the processing module 132 may extract from collecteddata a set of sensor values of the vehicle. The input parameters may beanalyzed by the one or more hidden layers 206 and a wear score generatedas the output 210. The wear score may be an integer value or a realnumber (e.g., a value from 0.0 to 1.0). Training an ML model to generatea wear score may include analyzing labeled wear scores corresponding toother vehicles.

For example, a training data set may include a data structure (e.g., ahash table), wherein the key of the hash table is a vehicleidentification number (VIN) of the vehicle, a first data value is a setof vehicle scores (e.g., by system or by individual component), and asecond data value is a vehicle wear score corresponding to the aggregatevehicle wear score of the vehicle. To train a wear score ML model, theML training module 164 may, for example, iterate over the hash table,inputting each of the sets of vehicle component scores as trainingparameters, and each respective vehicle wear score as a label parameterto the wear sore ML model, until a predetermined accuracy is achieved.The ML training module 164 may determine the accuracy of the model byminimizing a loss function.

Once the wear score ML model is trained, a module (e.g., the MLoperation module 166) may provide a de novo set of individual vehiclecomponent scores to the trained wear score ML model (i.e., operationalinputs) to obtain a wear score corresponding to the vehicle from whichthe collection module 130 collected the vehicle component scores. Thewear score predicted by the wear score ML model may be stored, inassociation with the VIN in an electronic database, such as the database190, and/or provided to another component (e.g., another ML model fortraining/operation).

The present techniques include several advantageous aspects, includingthat the present techniques work on every vehicle, using a solution thatdoes not need configuration for each specific vehicle. Further, bycombining key data elements, the present techniques generate outputs(e.g., resiliency of vehicle, feedback on driving behaviors, and howactions over time contribute to increase in risk of the vehicle overtime). These outputs are unique to the modeling approaches disclosedherein and are not available using conventional techniques.

Exemplary Wear and Tear Simulation Embodiment

For example, the ML training module 164 may retrieve a training data setfrom a database associated with the server 108. The training data setmay include a plurality of individual records, wherein each recordincludes a vehicle type, a set of vehicle operation behaviors, and avehicle age (as expressed as length of ownership and/or odometer). Eachof the training data set records may correspond to a particular vehicleoperator, and may be associated with a profile record, also stored inthe database.

The ML training module 164 may analyze the training data set to build amodel for analyzing a vehicle operator's vehicle operation behavior withrespect to a first vehicle to simulate the vehicle operator's vehicleoperation behavior with respect to a second vehicle. The ML trainingmodule may use the simulated vehicle operation behavior to predict wearand tear with respect to the second vehicle. The present techniquesadvantageously improve prior systems because the simulated vehicleoperation behavior is determined without needing sensors in the secondvehicle.

In some embodiments, the ML models may analyze historical driving datato simulate how vehicle operation behavior would affect wear and tear ofa vehicle the driver has not yet driven, or how present driving behaviorwould have affected a vehicle driven in the past for which no telematicsdata are available.

Exemplary Vehicle Operator Feedback Embodiment

In an embodiment, the present techniques are used to train an ML modelmay to accept telematics data corresponding to a vehicle operator (e.g.,braking events) and output a braking profile based at least in part uponanalyzing the braking information, wherein the ML model is trained usingtraining data corresponding to the braking behavior of other vehicleoperators.

For example, an unsupervised ML model may be used to group a set ofvehicle operators according to respective vehicle operation behaviors.The unsupervised ML model may quantify vehicle operation behaviors suchas hard braking, speed, and acceleration. The unsupervised ML model may,in some embodiments, determine whether the vehicle operator is speedingby analyzing mapping data. Once the unsupervised ML model has groupedthe vehicle operators into categories, the ML operation module 166 mayoperate a second ML model to analyze telematics data of a vehicleoperator not included in the training data set to determine whichcategory the vehicle operator not included in the training data set mostclosely resembles. In this way, the present techniques are able to gaugethe risk level of the vehicle operator, relative to other vehicleoperators.

The feedback module 170 may include instructions for providing feedbackto the vehicle operator in response to the vehicle operator'scategorization. For example, when the vehicle operator is found to be ina higher risk vehicle operation behavior category, the feedback module170 may generate a notification and transmit the notification to thetelematics system 104 for display. The notification may

Exemplary Risk Assessment Embodiment

In an embodiment, transfer learning may be used to simulate vehicleoperation behavior and wear outcomes. For example, each of a pluralityof vehicle operators may operate a respective vehicle. A first vehicleoperator in the plurality of vehicle operators may operate a firstvehicle lightly as measured by, for example, mileage, braking,acceleration, and overall wear. A second vehicle operator in theplurality of vehicle operators may operate the second vehicle so as tocause dramatically more wear by, for example, more mileage, morebraking, more acceleration, and generally more wear-causing behaviors.

In some embodiments, mileage may be determined by reference to thirdparty data sources (e.g., an application programming interface (API)) oran electronic database.

Respective instances of the telematics system 104 of FIG. 1 may beembodied in the respective mobile devices of the plurality of vehicleoperators, for example. The respective telematics systems 104 collecttelematics data of the two vehicle operators and store the telematicsdata in association with user profiles of the two vehicle operators. Forexample, the two vehicle operators may be customers of an insurer whodownload a computer application embodying the telematics system 104 intotheir respective smart phones.

The ML training module 164 may train a transfer ML model using thevehicle operation behavior and wear outcomes of the plurality of vehicleoperators. Specifically, the ML training module 164 may train thetransfer ML model to predict the wear to a vehicle by a vehicle operatornot in the plurality of vehicle operators, based at least in part uponthe similarity of the vehicle operator's vehicle operation behavior tothat of the plurality of vehicle operators. The transfer ML model mayalso predict the wear that one of the plurality of vehicle operatorswill cause to a new vehicle that the vehicle operator has not operatedpreviously, based at least in part upon the collected wear outcomes.Therefore, the transfer model may predict or estimate wear and tear(e.g., likelihood of brakes being worn thin) on first set of identicalcars based at least in part upon difference in wear/tear. In someembodiments, other conditions regarding the plurality of vehicleoperators may be used as training inputs to the transfer ML model, suchas accident information, which allows the link between driving behaviorto collisions to be seen.

The present techniques may analyze the plurality of vehicle operatorsfurther to determine whether risk is due to negative vehicle operationbehavior or contributions from wear and tear. Specifically, by analyzingthe length of ownership of vehicles within the plurality of vehicleoperators, the present techniques may determine that in general, vehicleoperators with a longer length of ownership are involved in morecollisions. An ML model may be trained that normalizes the plurality ofvehicle operators according to length of ownership, to determine thepartial effect of vehicle operation behavior on wear and tear, and howthe partial effect is correlated to losses. The trained model may acceptas inputs vehicle operation behavior, length of ownership (e.g.,odometer), and vehicle information and output an estimate of wear andtear to the vehicle. For example, the wear may be a vehicle wear score,a set of wear scores respective to particular components (e.g., brakes)or systems (e.g., cooling system). In particular, in some embodiments, ageneralized linear model (GLM) may be used to manually specifyinteractions and to multiply factors together, to estimate how eachfactor is correlated to a loss (e.g., the likelihood of a collision).

A user may adjust parameters of inputs to the trained model to assistthe user in reliability engineering. For example, with the trainedmodel, the user may change the value of the length of ownership from oneyear to ten years, to see how the longer ownership duration influencespredicted losses. The user may modify the vehicle type to determine howthe probability of collision may increase or decrease. Each vehicle inexistence may be ranked for comparative analyses. The user may alsoinput driving behaviors, to determine how wear and tear, and thus theprobability of collision, changes. The user may program simulations toautomatically provide inputs to the trained ML model, for determiningthe unique wear/tear to vehicles by varying length of ownership, formeasuring the resilience of vehicle types to wear/tear by changing theirtypes. Feedback may be provided to users based at least in part upon theeffect of modifying vehicle operation behaviors. For example, themessage may include a note to the user that that by avoiding hardbraking, wear and tear to the brake pads of the vehicle will bedecreased by, for example, 60%. In yet further embodiments, anautoencoder may be used to train a deep learning model that is able toanalyze all vehicles in existence. Specifically, a deep learning modelmay be trained to encode vehicle information into a smaller universe(e.g., into N-digit encoding). In this way, the N-digit encoding canexpress any possible vehicle that may exist.

Exemplary Computer-Implemented Methods

FIG. 4 depicts and exemplary method 400 for training a machine learningmodel. The method 400 may include retrieving/receiving a training dataset (block 402). The training data set may correspond to telematicsdata, profile data, and/or vehicle data. For example, the training dataset may be received/retrieved from the database 190. The method 400 mayinclude inputting the training data set into a machine learning model(block 404). In some embodiments, the method 400 may include inputtinglabeled data.

The method 400 may include analyzing the training input to generate aprediction (block 406). The method 400 may include generating the lossscore by comparing the prediction to the label using a loss function(block 408). In embodiments wherein the machine learning model is anartificial neural network, the method 400 may include backpropagatingthe loss score to update the set of weights, wherein the method 400trains the model repeatedly to adjust a set of weights of the machinelearning model, until a loss score meets a predetermined criteria.Training, discussed in further detail above, may be carried out by theML training module 164 of Figure. It should be appreciated that in somecases, the machine learning model may be trained without using anylabeled information (e.g., in unsupervised learning). Further, in someembodiments, the method 400 may include storing a set of weights as theweights of the trained machine learning model. The method 400 mayinclude storing the trained machine learning model once the loss scoremeets a predetermined criteria (block 410). Specifically, the MLtraining module 164 may serialize and store the weights of the networkin an electronic database or on a disk (e.g., the memory 162) asdiscussed above. Another module (e.g., the ML operation module 168) mayoperate the machine learning model (e.g., using the stored weightsand/or other parameterization/initialization data, such ashyperparameters).

The method 400 may train one or more ML model for a variety of tasks,including calculating a vehicle wear score, simulating wear and tearaccording to vehicle operator behavior, generating feedback for thevehicle operator, and generating a risk assessment demonstrating showinghow driving behavior is correlated to risk over time. Once the method400 has trained the machine learning model, the method 400 may includereceiving and processing operational telematics information (e.g., froma mobile device of a user) as discussed above.

For example, historical data may demonstrate that a given percentage ofcollisions are due to vehicle maintenance problems (mechanical failuredue to wear and tear). Further, wear and tear may be substantiallyaffected by individual vehicle operation behaviors. Thus, vehicleoperation behaviors may be used to train an ML model to predict riskpricing. A target variable of such an ML model is whether the vehiclewill be involved in a collision due to a failure attributable to wearand tear. It should be appreciated that in an embodiment, telematicsdata are not used to train such a model. Rather, an encoding may includea type of the vehicle (e.g., year, make, and model), driving behaviorsof the vehicle operator (e.g., braking events, and speeding) and thelength of ownership (e.g., odometer or time). Such information may beused as features for the model to predict whether will be involved in aclaim attributable to normal wear and tear.

Although specific embodiments of the present disclosure have beendescribed, it will be understood by those of skill in the art that thereare other embodiments that are equivalent to the described embodiments.Accordingly, it is to be understood that the present disclosure is notto be limited by the specific illustrated embodiments.

1. A non-transitory computer readable medium storing instructionsthereon, when executed by one or more processors, causing the one ormore processors perform operations comprising: retrieving a machinelearning model; wherein the machine learning model is trained until aloss score meets a predetermined criteria by at least: inputting atraining input and corresponding label, analyzing the training inputusing the machine learning model to generate a training prediction, andgenerating the loss score by comparing the training prediction to thecorresponding label using a loss function, and modifying at least one ofone or more weights and one or more biases of the machine learning modelbased at least in part upon the loss score; receiving an operationalinput; and analyzing the operational input using the trained machinelearning model to obtain a prediction; wherein: the operational inputincludes a set of component wear scores of a vehicle, and the predictionincludes an aggregated wear score of the vehicle generated by analyzingthe set of component wear scores.
 2. (canceled)
 3. The non-transitorycomputer readable medium of claim 1, wherein the operations furthercomprise: analyzing the aggregated wear score and type information ofthe vehicle to predict one or more general wear and tear characteristicsof the vehicle.
 4. The non-transitory computer readable medium of claim1, wherein: the operational input includes telematics data correspondingto one or more vehicles operated by an operator of the vehicle, vehicletype data, or vehicle age; and the prediction includes an estimated wearof the vehicle.
 5. The non-transitory computer readable medium of claim1, wherein the operational input is adjusted programmatically or by auser to simulate a partial effect.
 6. The non-transitory computerreadable medium of claim 1, wherein the operational input includestelematics data, and wherein the analyzing the operational input usingthe trained machine learning model to obtain a prediction includesanalyzing the telematics data to categorize the collision risk of avehicle operator of the vehicle.
 7. The non-transitory computer readablemedium of claim 6, wherein the operations further comprise: transmittinga feedback notification to the vehicle operator, the feedbacknotification including a warning in regard to the collision risk and arecommendation for reducing the collision risk.
 8. The non-transitorycomputer readable medium of claim 1, wherein the trained machinelearning model includes a first machine learning model to determine avehicle wear score and a second machine learning model to generate arisk assessment.
 9. A machine learning training and operation servercomprising: one or more processors; and a memory storing instructionsthat, when executed by the one or more processors, cause the server to:retrieve a machine learning model; wherein the machine learning model istrained until a loss score meets a predetermined criteria by at least:inputting a training input and corresponding label; analyzing thetraining input to generate a training prediction; generating the lossscore by comparing the training prediction to the label using a lossfunction; modifying at least one of one or more weights and one or morebiases of the machine learning model based at least in part upon theloss score; instantiate the trained machine learning model; receive anoperational input; and analyze the operational input using the trainedmachine learning model to obtain a prediction; wherein: the operationalinput includes a set of component wear scores of a vehicle, and theprediction includes an aggregated wear score of the vehicle generated byanalyzing the set of component wear scores.
 10. (canceled)
 11. Theserver of claim 9, wherein the instructions further cause the server to:analyze the aggregated wear score and type information of the vehicle topredict one or more general wear and tear characteristics of thevehicle.
 12. The server of claim 9, wherein the instructions furthercause the server to: receive telematics data corresponding to one ormore vehicles operated by an operator of the vehicle, vehicle type data,or vehicle age; and generate an estimated wear of the vehicle.
 13. Theserver of claim 9, wherein the trained machine learning model includes afirst machine learning model to determine a vehicle wear score and asecond machine learning model to generate a risk assessment.
 14. Acomputer-implemented method, the method including: retrieving, via oneor more processors, a machine learning model; wherein the machinelearning model is trained until a loss score meets a predeterminedcriteria by at least: inputting to the machine learning model a traininginput and corresponding label, analyzing the training input using themachine learning model to generate a training prediction, generating theloss score by comparing the training prediction to the label using aloss function; modifying at least one of one or more weights and one ormore biases of the machine learning model based at least in part uponthe loss score; instantiating the trained machine learning model;receiving an operational input; analyzing the operational input usingthe trained machine learning model to obtain a prediction; wherein: theoperational input includes a set of component wear scores of a vehicle,and the prediction includes an aggregated wear score of the vehiclegenerated by analyzing the set of component wear scores.
 15. (canceled)16. The computer-implemented method of claim 14, further comprising:analyzing the aggregated wear score and type information of the vehicleto predict one or more general wear and tear characteristics of thevehicle.
 17. The computer-implemented method of claim 14, wherein: theoperational input includes telematics data corresponding to one or morevehicles operated by an operator of the vehicle, vehicle type data, orvehicle age; and the prediction corresponds to an estimated wear of thevehicle.
 18. The computer-implemented method of claim 14, wherein theoperational input includes telematics data, wherein the analyzing theoperational input to obtain a prediction includes analyzing thetelematics data to categorize collision risk of a vehicle operator ofthe vehicle.
 19. The computer-implemented method of claim 18, furthercomprising: transmitting a feedback notification to the vehicleoperator, the feedback notification including a warning in regard tocollision risk and a recommendation for reducing the collision risk. 20.The computer-implemented method of claim 14, wherein the trained machinelearning model includes a first machine learning model to determine avehicle wear score and a second machine learning model to generate arisk assessment.